The present invention relates to behavior change detection and, more particularly, to a method for regional human behavior change detection from utility consumption.
Regional human behavior change refers to scenarios in which people in a certain area exhibit significant behavior deviation from their neighbors and their own past. This regional pattern provides important information for urban planning, public security, disease control and sales marketing. Data reflective of regional human behavior change usually reveals underlying changes of living environment, such as regional development, immigration and/or disease breakout and may uncover demographic information from special events such as, for example, start/end of school, holidays or religious holidays. Statistically significant behavior changes exhibit both temporal and spatial characteristics.
Using utility consumption to identify regional behavior change provides for a solution toward analyzing human behavior based on widely, if not publicly, available information. Because of the recent quick development of smart meter infrastructures, this solution becomes possible. However, existing statistic approaches for regional outlier detection do not consider multiple distributions of data, which may lead to failed detection of multiple local outlier regions. In addition, these approaches generally do not provide data-driven scan windows or scalable data access for large data sets.